An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm

The random forest (RF) algorithm is a typical representative of ensemble learning, which is widely used in rolling bearing fault diagnosis. In order to solve the problems of slower diagnosis speed and repeated voting of traditional RF algorithm in rolling bearing fault diagnosis under the big data e...

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Main Authors:	Lanjun Wan, Kun Gong, Gen Zhang, Xinpan Yuan, Changyun Li, Xiaojun Deng
Format:	article
Language:	EN
Published:	IEEE 2021
Subjects:	Fault diagnosis random forest rolling bearing spark platform sub-forest optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971
Online Access:	https://doaj.org/article/e1eb03d32f8d4c5cadc4ece4b943b137
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spelling	oai:doaj.org-article:e1eb03d32f8d4c5cadc4ece4b943b1372021-11-20T00:00:35ZAn Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm2169-353610.1109/ACCESS.2021.3063929https://doaj.org/article/e1eb03d32f8d4c5cadc4ece4b943b1372021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9369361/https://doaj.org/toc/2169-3536The random forest (RF) algorithm is a typical representative of ensemble learning, which is widely used in rolling bearing fault diagnosis. In order to solve the problems of slower diagnosis speed and repeated voting of traditional RF algorithm in rolling bearing fault diagnosis under the big data environment, an efficient rolling bearing fault diagnosis method based on Spark and improved random forest (IRF) algorithm is proposed. By eliminating the decision trees with low classification accuracy and those prone to repeated voting in the original RF, an improved RF with faster diagnosis speed and higher classification accuracy is constructed. For the massive rolling bearing vibration data, in order to improve the training speed and diagnosis speed of the rolling bearing fault diagnosis model, the IRF algorithm is parallelized on the Spark platform. First, an original RF model is obtained by training multiple decision trees in parallel. Second, the decision trees with low classification accuracy in the original RF model are filtered. Third, all path information of the reserved decision trees is obtained in parallel. Fourth, a decision tree similarity matrix is constructed in parallel to eliminate the decision trees which are prone to repeated voting. Finally, an IRF model which can diagnose rolling bearing faults quickly and effectively is obtained. A series of experiments are carried out to evaluate the effectiveness of the proposed rolling bearing fault diagnosis method based on Spark and IRF algorithm. The results show that the proposed method can not only achieve good fault diagnosis accuracy, but also have fast model training speed and fault diagnosis speed for large-scale rolling bearing datasets.Lanjun WanKun GongGen ZhangXinpan YuanChangyun LiXiaojun DengIEEEarticleFault diagnosisrandom forestrolling bearingspark platformsub-forest optimizationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 37866-37882 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Fault diagnosis random forest rolling bearing spark platform sub-forest optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Fault diagnosis random forest rolling bearing spark platform sub-forest optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971 Lanjun Wan Kun Gong Gen Zhang Xinpan Yuan Changyun Li Xiaojun Deng An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm
description	The random forest (RF) algorithm is a typical representative of ensemble learning, which is widely used in rolling bearing fault diagnosis. In order to solve the problems of slower diagnosis speed and repeated voting of traditional RF algorithm in rolling bearing fault diagnosis under the big data environment, an efficient rolling bearing fault diagnosis method based on Spark and improved random forest (IRF) algorithm is proposed. By eliminating the decision trees with low classification accuracy and those prone to repeated voting in the original RF, an improved RF with faster diagnosis speed and higher classification accuracy is constructed. For the massive rolling bearing vibration data, in order to improve the training speed and diagnosis speed of the rolling bearing fault diagnosis model, the IRF algorithm is parallelized on the Spark platform. First, an original RF model is obtained by training multiple decision trees in parallel. Second, the decision trees with low classification accuracy in the original RF model are filtered. Third, all path information of the reserved decision trees is obtained in parallel. Fourth, a decision tree similarity matrix is constructed in parallel to eliminate the decision trees which are prone to repeated voting. Finally, an IRF model which can diagnose rolling bearing faults quickly and effectively is obtained. A series of experiments are carried out to evaluate the effectiveness of the proposed rolling bearing fault diagnosis method based on Spark and IRF algorithm. The results show that the proposed method can not only achieve good fault diagnosis accuracy, but also have fast model training speed and fault diagnosis speed for large-scale rolling bearing datasets.
format	article
author	Lanjun Wan Kun Gong Gen Zhang Xinpan Yuan Changyun Li Xiaojun Deng
author_facet	Lanjun Wan Kun Gong Gen Zhang Xinpan Yuan Changyun Li Xiaojun Deng
author_sort	Lanjun Wan
title	An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm
title_short	An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm
title_full	An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm
title_fullStr	An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm
title_full_unstemmed	An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm
title_sort	efficient rolling bearing fault diagnosis method based on spark and improved random forest algorithm
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/e1eb03d32f8d4c5cadc4ece4b943b137
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An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm

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